When treating tumors, the effectiveness of a treatment is determined by analyzing tumor size, comparing the size pre-treatment and post-treatment. Typically, tumor size is measured through an analysis of magnetic resonance imaging (MRI) or other image data. Such techniques have been used for years and are relatively effective. However, unfortunately, a tumor size change often occurs late, and therefore the patient loses the time window for optimal treatment.
In recent years, physiological, metabolic and molecular imaging has been developed and tested for early prediction of tumor treatment response and outcome with a promise that a biological change in the tumor could occur prior to its size change. Analysis of physiological, metabolic and molecular imaging in the tumor is often done by averaging the values of the relevant physiological imaging parameter in the tumor and then comparing the mean values between pre and post therapy. This is a simple but not effective approach due to neglecting the heterogeneous distribution of the physiological parameter in the tumor, particularly in large tumors. An average value of a physiological imaging parameter in the tumor can wash out the sensitivity of the parameter to a change in the tumor. Indeed, this sensitivity problem is a direct result of the inability of existing techniques to effectively discriminate the information within a tumor image. Tumor image data may reflect numerous different phenotypes/conditions within a tissue mass, but without an ability to properly discriminate between these different phenotypes/conditions, from a medical image data, effective analysis and diagnosis is limited.
A voxel-by-voxel analysis of a change in the physiological, metabolic or molecular imaging parameter of the tumor pre and post therapy is an improvement but requires an accurate registration of a pair of images obtained pre and post therapy. When there is tumor growth and shrinkage from pre to post therapy, the results of the voxel-by-voxel analysis of the physiological imaging are often incorrect or, at a minimum, misleading. The reasons for this are numerous, but the primary culprit is mis-registration of image data at the voxel level. As a tumor changes in size, it is difficult to register with satisfactory certainty a first image of a tumor, taken at one point, e.g., pre-treatment, with the second image of that same tumor, now changed, taken at a second point, e.g., post treatment. This image registration problem commonly requires complex image analysis to correct and still limits the effectiveness of practitioners to use physiological, metabolic and molecular imaging for proper tumor treatment assessment.
To derive a physiological imaging parameter from dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data or other dynamic PET/SPECT data involves time-consuming pharmacokinetic modeling. Trying to diagnose physiological conditions from that image parameter is prone to error, poor reproducibility and lack of accuracy. For example, in analyzing a physiological imaging parameter of a tumor for therapy response, a parameter such as Gd-DTPA (gadopentetic acid) transfer constant (Ktrans) derived from DCE-MRI by a pharmacokinetic model is sensitive to noise in the DCE-MRI, and has a reproducibility of approximately 20%, which reduces the minimum change that can be detected post therapy compared to pre therapy. Ultimately, medical imaging is used in tumor treatment to determine which treatments are more effective and where to direct those treatments within the patient. For example, intensity-modulated radiotherapy (IMRT) seeks to deliver high-precision nonuniform dose patterns through ‘painting’ and ‘sculpting’ doses in a radiation target volume in order to improve the therapeutic ratio and treatment outcome. The conventional IMRT attempts to optimize and deliver a treatment plan having a uniform dose distribution within a target volume delineated primarily based upon anatomic images of computed tomography (CT) and/or MRI. Geometrically conforming high doses within the target volume by IMRT can reduce dose-spread into normal tissue and organs at risk. However, target volume delineation based upon anatomic information provided by CT images and MRI images is limited. Also, considering spatially-heterogeneous biological properties of a tumor, a uniform dose distribution within a target volume might not lead to an optimal treatment outcome.
In any event, voxel-by-voxel image analyses, while effective to an extent, are limiting. A more useful approach is desired.